{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data path is assumed to be 'data' under repo root\n",
    "def csv_loader(filename, datapath = \"./data\", stats = True):\n",
    "    print('Loading %s.' % filename)\n",
    "    path = os.path.join(datapath, filename)\n",
    "    try:\n",
    "        with open(path, 'rb') as f:\n",
    "              data = pd.read_csv(f)\n",
    "    except Exception as e:\n",
    "        print('Unable to load data ', path, ':', e)\n",
    "    if stats:\n",
    "        print(\"{:d} rows of data loaded.\".format(len(data)))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading application_train.csv.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         0.0  \n",
       "\n",
       "[3 rows x 122 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = csv_loader(\"application_train.csv\")\n",
    "df_train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307511 data, 122 columns, 16 catagorical\n"
     ]
    }
   ],
   "source": [
    "col_categorical = [col for col in df_train.columns if df_train[col].dtype == 'object']\n",
    "\n",
    "print(\"{:d} data, {:d} columns, {:d} catagorical\".format(len(df_train), len(df_train.columns), len(col_categorical)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SK_ID_CURR                               int64\n",
      "TARGET                                   int64\n",
      "NAME_CONTRACT_TYPE                       object\n",
      "CODE_GENDER                              object\n",
      "FLAG_OWN_CAR                             object\n",
      "FLAG_OWN_REALTY                          object\n",
      "CNT_CHILDREN                             int64\n",
      "AMT_INCOME_TOTAL                         float64\n",
      "AMT_CREDIT                               float64\n",
      "AMT_ANNUITY                              float64\n",
      "AMT_GOODS_PRICE                          float64\n",
      "NAME_TYPE_SUITE                          object\n",
      "NAME_INCOME_TYPE                         object\n",
      "NAME_EDUCATION_TYPE                      object\n",
      "NAME_FAMILY_STATUS                       object\n",
      "NAME_HOUSING_TYPE                        object\n",
      "REGION_POPULATION_RELATIVE               float64\n",
      "DAYS_BIRTH                               int64\n",
      "DAYS_EMPLOYED                            int64\n",
      "DAYS_REGISTRATION                        float64\n",
      "DAYS_ID_PUBLISH                          int64\n",
      "OWN_CAR_AGE                              float64\n",
      "FLAG_MOBIL                               int64\n",
      "FLAG_EMP_PHONE                           int64\n",
      "FLAG_WORK_PHONE                          int64\n",
      "FLAG_CONT_MOBILE                         int64\n",
      "FLAG_PHONE                               int64\n",
      "FLAG_EMAIL                               int64\n",
      "OCCUPATION_TYPE                          object\n",
      "CNT_FAM_MEMBERS                          float64\n",
      "REGION_RATING_CLIENT                     int64\n",
      "REGION_RATING_CLIENT_W_CITY              int64\n",
      "WEEKDAY_APPR_PROCESS_START               object\n",
      "HOUR_APPR_PROCESS_START                  int64\n",
      "REG_REGION_NOT_LIVE_REGION               int64\n",
      "REG_REGION_NOT_WORK_REGION               int64\n",
      "LIVE_REGION_NOT_WORK_REGION              int64\n",
      "REG_CITY_NOT_LIVE_CITY                   int64\n",
      "REG_CITY_NOT_WORK_CITY                   int64\n",
      "LIVE_CITY_NOT_WORK_CITY                  int64\n",
      "ORGANIZATION_TYPE                        object\n",
      "EXT_SOURCE_1                             float64\n",
      "EXT_SOURCE_2                             float64\n",
      "EXT_SOURCE_3                             float64\n",
      "APARTMENTS_AVG                           float64\n",
      "BASEMENTAREA_AVG                         float64\n",
      "YEARS_BEGINEXPLUATATION_AVG              float64\n",
      "YEARS_BUILD_AVG                          float64\n",
      "COMMONAREA_AVG                           float64\n",
      "ELEVATORS_AVG                            float64\n",
      "ENTRANCES_AVG                            float64\n",
      "FLOORSMAX_AVG                            float64\n",
      "FLOORSMIN_AVG                            float64\n",
      "LANDAREA_AVG                             float64\n",
      "LIVINGAPARTMENTS_AVG                     float64\n",
      "LIVINGAREA_AVG                           float64\n",
      "NONLIVINGAPARTMENTS_AVG                  float64\n",
      "NONLIVINGAREA_AVG                        float64\n",
      "APARTMENTS_MODE                          float64\n",
      "BASEMENTAREA_MODE                        float64\n",
      "YEARS_BEGINEXPLUATATION_MODE             float64\n",
      "YEARS_BUILD_MODE                         float64\n",
      "COMMONAREA_MODE                          float64\n",
      "ELEVATORS_MODE                           float64\n",
      "ENTRANCES_MODE                           float64\n",
      "FLOORSMAX_MODE                           float64\n",
      "FLOORSMIN_MODE                           float64\n",
      "LANDAREA_MODE                            float64\n",
      "LIVINGAPARTMENTS_MODE                    float64\n",
      "LIVINGAREA_MODE                          float64\n",
      "NONLIVINGAPARTMENTS_MODE                 float64\n",
      "NONLIVINGAREA_MODE                       float64\n",
      "APARTMENTS_MEDI                          float64\n",
      "BASEMENTAREA_MEDI                        float64\n",
      "YEARS_BEGINEXPLUATATION_MEDI             float64\n",
      "YEARS_BUILD_MEDI                         float64\n",
      "COMMONAREA_MEDI                          float64\n",
      "ELEVATORS_MEDI                           float64\n",
      "ENTRANCES_MEDI                           float64\n",
      "FLOORSMAX_MEDI                           float64\n",
      "FLOORSMIN_MEDI                           float64\n",
      "LANDAREA_MEDI                            float64\n",
      "LIVINGAPARTMENTS_MEDI                    float64\n",
      "LIVINGAREA_MEDI                          float64\n",
      "NONLIVINGAPARTMENTS_MEDI                 float64\n",
      "NONLIVINGAREA_MEDI                       float64\n",
      "FONDKAPREMONT_MODE                       object\n",
      "HOUSETYPE_MODE                           object\n",
      "TOTALAREA_MODE                           float64\n",
      "WALLSMATERIAL_MODE                       object\n",
      "EMERGENCYSTATE_MODE                      object\n",
      "OBS_30_CNT_SOCIAL_CIRCLE                 float64\n",
      "DEF_30_CNT_SOCIAL_CIRCLE                 float64\n",
      "OBS_60_CNT_SOCIAL_CIRCLE                 float64\n",
      "DEF_60_CNT_SOCIAL_CIRCLE                 float64\n",
      "DAYS_LAST_PHONE_CHANGE                   float64\n",
      "FLAG_DOCUMENT_2                          int64\n",
      "FLAG_DOCUMENT_3                          int64\n",
      "FLAG_DOCUMENT_4                          int64\n",
      "FLAG_DOCUMENT_5                          int64\n",
      "FLAG_DOCUMENT_6                          int64\n",
      "FLAG_DOCUMENT_7                          int64\n",
      "FLAG_DOCUMENT_8                          int64\n",
      "FLAG_DOCUMENT_9                          int64\n",
      "FLAG_DOCUMENT_10                         int64\n",
      "FLAG_DOCUMENT_11                         int64\n",
      "FLAG_DOCUMENT_12                         int64\n",
      "FLAG_DOCUMENT_13                         int64\n",
      "FLAG_DOCUMENT_14                         int64\n",
      "FLAG_DOCUMENT_15                         int64\n",
      "FLAG_DOCUMENT_16                         int64\n",
      "FLAG_DOCUMENT_17                         int64\n",
      "FLAG_DOCUMENT_18                         int64\n",
      "FLAG_DOCUMENT_19                         int64\n",
      "FLAG_DOCUMENT_20                         int64\n",
      "FLAG_DOCUMENT_21                         int64\n",
      "AMT_REQ_CREDIT_BUREAU_HOUR               float64\n",
      "AMT_REQ_CREDIT_BUREAU_DAY                float64\n",
      "AMT_REQ_CREDIT_BUREAU_WEEK               float64\n",
      "AMT_REQ_CREDIT_BUREAU_MON                float64\n",
      "AMT_REQ_CREDIT_BUREAU_QRT                float64\n",
      "AMT_REQ_CREDIT_BUREAU_YEAR               float64\n"
     ]
    }
   ],
   "source": [
    "for col in df_train.columns:\n",
    "    print(\"{:40s}\".format(col), df_train[col].dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK_ID_CURR                     0\n",
       "TARGET                         0\n",
       "CNT_CHILDREN                   0\n",
       "DAYS_BIRTH                     0\n",
       "DAYS_EMPLOYED                  0\n",
       "DAYS_ID_PUBLISH                0\n",
       "FLAG_MOBIL                     0\n",
       "FLAG_EMP_PHONE                 0\n",
       "FLAG_WORK_PHONE                0\n",
       "FLAG_CONT_MOBILE               0\n",
       "FLAG_PHONE                     0\n",
       "FLAG_EMAIL                     0\n",
       "REGION_RATING_CLIENT           0\n",
       "REGION_RATING_CLIENT_W_CITY    0\n",
       "HOUR_APPR_PROCESS_START        0\n",
       "REG_REGION_NOT_LIVE_REGION     0\n",
       "REG_REGION_NOT_WORK_REGION     0\n",
       "LIVE_REGION_NOT_WORK_REGION    0\n",
       "REG_CITY_NOT_LIVE_CITY         0\n",
       "REG_CITY_NOT_WORK_CITY         0\n",
       "LIVE_CITY_NOT_WORK_CITY        0\n",
       "FLAG_DOCUMENT_2                0\n",
       "FLAG_DOCUMENT_3                0\n",
       "FLAG_DOCUMENT_4                0\n",
       "FLAG_DOCUMENT_5                0\n",
       "FLAG_DOCUMENT_6                0\n",
       "FLAG_DOCUMENT_7                0\n",
       "FLAG_DOCUMENT_8                0\n",
       "FLAG_DOCUMENT_9                0\n",
       "FLAG_DOCUMENT_10               0\n",
       "FLAG_DOCUMENT_11               0\n",
       "FLAG_DOCUMENT_12               0\n",
       "FLAG_DOCUMENT_13               0\n",
       "FLAG_DOCUMENT_14               0\n",
       "FLAG_DOCUMENT_15               0\n",
       "FLAG_DOCUMENT_16               0\n",
       "FLAG_DOCUMENT_17               0\n",
       "FLAG_DOCUMENT_18               0\n",
       "FLAG_DOCUMENT_19               0\n",
       "FLAG_DOCUMENT_20               0\n",
       "FLAG_DOCUMENT_21               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_int = [col for col in df_train.columns if df_train[col].dtype == 'int64']\n",
    "df_train[col_int].isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>DAYS_EMPLOYED</th>\n",
       "      <th>DAYS_ID_PUBLISH</th>\n",
       "      <th>FLAG_MOBIL</th>\n",
       "      <th>FLAG_EMP_PHONE</th>\n",
       "      <th>FLAG_WORK_PHONE</th>\n",
       "      <th>FLAG_CONT_MOBILE</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_12</th>\n",
       "      <th>FLAG_DOCUMENT_13</th>\n",
       "      <th>FLAG_DOCUMENT_14</th>\n",
       "      <th>FLAG_DOCUMENT_15</th>\n",
       "      <th>FLAG_DOCUMENT_16</th>\n",
       "      <th>FLAG_DOCUMENT_17</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.00000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>278180.518577</td>\n",
       "      <td>0.080729</td>\n",
       "      <td>0.417052</td>\n",
       "      <td>-16036.995067</td>\n",
       "      <td>63815.045904</td>\n",
       "      <td>-2994.202373</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.819889</td>\n",
       "      <td>0.199368</td>\n",
       "      <td>0.998133</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.003525</td>\n",
       "      <td>0.002936</td>\n",
       "      <td>0.00121</td>\n",
       "      <td>0.009928</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.008130</td>\n",
       "      <td>0.000595</td>\n",
       "      <td>0.000507</td>\n",
       "      <td>0.000335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102790.175348</td>\n",
       "      <td>0.272419</td>\n",
       "      <td>0.722121</td>\n",
       "      <td>4363.988632</td>\n",
       "      <td>141275.766519</td>\n",
       "      <td>1509.450419</td>\n",
       "      <td>0.001803</td>\n",
       "      <td>0.384280</td>\n",
       "      <td>0.399526</td>\n",
       "      <td>0.043164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002550</td>\n",
       "      <td>0.059268</td>\n",
       "      <td>0.054110</td>\n",
       "      <td>0.03476</td>\n",
       "      <td>0.099144</td>\n",
       "      <td>0.016327</td>\n",
       "      <td>0.089798</td>\n",
       "      <td>0.024387</td>\n",
       "      <td>0.022518</td>\n",
       "      <td>0.018299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>100002.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-25229.000000</td>\n",
       "      <td>-17912.000000</td>\n",
       "      <td>-7197.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189145.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-19682.000000</td>\n",
       "      <td>-2760.000000</td>\n",
       "      <td>-4299.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>278202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-15750.000000</td>\n",
       "      <td>-1213.000000</td>\n",
       "      <td>-3254.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>367142.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-12413.000000</td>\n",
       "      <td>-289.000000</td>\n",
       "      <td>-1720.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>456255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>-7489.000000</td>\n",
       "      <td>365243.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SK_ID_CURR         TARGET   CNT_CHILDREN     DAYS_BIRTH  \\\n",
       "count  307511.000000  307511.000000  307511.000000  307511.000000   \n",
       "mean   278180.518577       0.080729       0.417052  -16036.995067   \n",
       "std    102790.175348       0.272419       0.722121    4363.988632   \n",
       "min    100002.000000       0.000000       0.000000  -25229.000000   \n",
       "25%    189145.500000       0.000000       0.000000  -19682.000000   \n",
       "50%    278202.000000       0.000000       0.000000  -15750.000000   \n",
       "75%    367142.500000       0.000000       1.000000  -12413.000000   \n",
       "max    456255.000000       1.000000      19.000000   -7489.000000   \n",
       "\n",
       "       DAYS_EMPLOYED  DAYS_ID_PUBLISH     FLAG_MOBIL  FLAG_EMP_PHONE  \\\n",
       "count  307511.000000    307511.000000  307511.000000   307511.000000   \n",
       "mean    63815.045904     -2994.202373       0.999997        0.819889   \n",
       "std    141275.766519      1509.450419       0.001803        0.384280   \n",
       "min    -17912.000000     -7197.000000       0.000000        0.000000   \n",
       "25%     -2760.000000     -4299.000000       1.000000        1.000000   \n",
       "50%     -1213.000000     -3254.000000       1.000000        1.000000   \n",
       "75%      -289.000000     -1720.000000       1.000000        1.000000   \n",
       "max    365243.000000         0.000000       1.000000        1.000000   \n",
       "\n",
       "       FLAG_WORK_PHONE  FLAG_CONT_MOBILE        ...         FLAG_DOCUMENT_12  \\\n",
       "count    307511.000000     307511.000000        ...            307511.000000   \n",
       "mean          0.199368          0.998133        ...                 0.000007   \n",
       "std           0.399526          0.043164        ...                 0.002550   \n",
       "min           0.000000          0.000000        ...                 0.000000   \n",
       "25%           0.000000          1.000000        ...                 0.000000   \n",
       "50%           0.000000          1.000000        ...                 0.000000   \n",
       "75%           0.000000          1.000000        ...                 0.000000   \n",
       "max           1.000000          1.000000        ...                 1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_13  FLAG_DOCUMENT_14  FLAG_DOCUMENT_15  FLAG_DOCUMENT_16  \\\n",
       "count     307511.000000     307511.000000      307511.00000     307511.000000   \n",
       "mean           0.003525          0.002936           0.00121          0.009928   \n",
       "std            0.059268          0.054110           0.03476          0.099144   \n",
       "min            0.000000          0.000000           0.00000          0.000000   \n",
       "25%            0.000000          0.000000           0.00000          0.000000   \n",
       "50%            0.000000          0.000000           0.00000          0.000000   \n",
       "75%            0.000000          0.000000           0.00000          0.000000   \n",
       "max            1.000000          1.000000           1.00000          1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_17  FLAG_DOCUMENT_18  FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  \\\n",
       "count     307511.000000     307511.000000     307511.000000     307511.000000   \n",
       "mean           0.000267          0.008130          0.000595          0.000507   \n",
       "std            0.016327          0.089798          0.024387          0.022518   \n",
       "min            0.000000          0.000000          0.000000          0.000000   \n",
       "25%            0.000000          0.000000          0.000000          0.000000   \n",
       "50%            0.000000          0.000000          0.000000          0.000000   \n",
       "75%            0.000000          0.000000          0.000000          0.000000   \n",
       "max            1.000000          1.000000          1.000000          1.000000   \n",
       "\n",
       "       FLAG_DOCUMENT_21  \n",
       "count     307511.000000  \n",
       "mean           0.000335  \n",
       "std            0.018299  \n",
       "min            0.000000  \n",
       "25%            0.000000  \n",
       "50%            0.000000  \n",
       "75%            0.000000  \n",
       "max            1.000000  \n",
       "\n",
       "[8 rows x 41 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_int].describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AMT_INCOME_TOTAL                     0\n",
       "AMT_CREDIT                           0\n",
       "AMT_ANNUITY                         12\n",
       "AMT_GOODS_PRICE                    278\n",
       "REGION_POPULATION_RELATIVE           0\n",
       "DAYS_REGISTRATION                    0\n",
       "OWN_CAR_AGE                     202929\n",
       "CNT_FAM_MEMBERS                      2\n",
       "EXT_SOURCE_1                    173378\n",
       "EXT_SOURCE_2                       660\n",
       "EXT_SOURCE_3                     60965\n",
       "APARTMENTS_AVG                  156061\n",
       "BASEMENTAREA_AVG                179943\n",
       "YEARS_BEGINEXPLUATATION_AVG     150007\n",
       "YEARS_BUILD_AVG                 204488\n",
       "COMMONAREA_AVG                  214865\n",
       "ELEVATORS_AVG                   163891\n",
       "ENTRANCES_AVG                   154828\n",
       "FLOORSMAX_AVG                   153020\n",
       "FLOORSMIN_AVG                   208642\n",
       "LANDAREA_AVG                    182590\n",
       "LIVINGAPARTMENTS_AVG            210199\n",
       "LIVINGAREA_AVG                  154350\n",
       "NONLIVINGAPARTMENTS_AVG         213514\n",
       "NONLIVINGAREA_AVG               169682\n",
       "APARTMENTS_MODE                 156061\n",
       "BASEMENTAREA_MODE               179943\n",
       "YEARS_BEGINEXPLUATATION_MODE    150007\n",
       "YEARS_BUILD_MODE                204488\n",
       "COMMONAREA_MODE                 214865\n",
       "                                 ...  \n",
       "LIVINGAPARTMENTS_MODE           210199\n",
       "LIVINGAREA_MODE                 154350\n",
       "NONLIVINGAPARTMENTS_MODE        213514\n",
       "NONLIVINGAREA_MODE              169682\n",
       "APARTMENTS_MEDI                 156061\n",
       "BASEMENTAREA_MEDI               179943\n",
       "YEARS_BEGINEXPLUATATION_MEDI    150007\n",
       "YEARS_BUILD_MEDI                204488\n",
       "COMMONAREA_MEDI                 214865\n",
       "ELEVATORS_MEDI                  163891\n",
       "ENTRANCES_MEDI                  154828\n",
       "FLOORSMAX_MEDI                  153020\n",
       "FLOORSMIN_MEDI                  208642\n",
       "LANDAREA_MEDI                   182590\n",
       "LIVINGAPARTMENTS_MEDI           210199\n",
       "LIVINGAREA_MEDI                 154350\n",
       "NONLIVINGAPARTMENTS_MEDI        213514\n",
       "NONLIVINGAREA_MEDI              169682\n",
       "TOTALAREA_MODE                  148431\n",
       "OBS_30_CNT_SOCIAL_CIRCLE          1021\n",
       "DEF_30_CNT_SOCIAL_CIRCLE          1021\n",
       "OBS_60_CNT_SOCIAL_CIRCLE          1021\n",
       "DEF_60_CNT_SOCIAL_CIRCLE          1021\n",
       "DAYS_LAST_PHONE_CHANGE               1\n",
       "AMT_REQ_CREDIT_BUREAU_HOUR       41519\n",
       "AMT_REQ_CREDIT_BUREAU_DAY        41519\n",
       "AMT_REQ_CREDIT_BUREAU_WEEK       41519\n",
       "AMT_REQ_CREDIT_BUREAU_MON        41519\n",
       "AMT_REQ_CREDIT_BUREAU_QRT        41519\n",
       "AMT_REQ_CREDIT_BUREAU_YEAR       41519\n",
       "Length: 65, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_num = [col for col in df_train.columns if df_train[col].dtype == 'float64']\n",
    "df_train[col_num].isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>REGION_POPULATION_RELATIVE</th>\n",
       "      <th>DAYS_REGISTRATION</th>\n",
       "      <th>OWN_CAR_AGE</th>\n",
       "      <th>CNT_FAM_MEMBERS</th>\n",
       "      <th>EXT_SOURCE_1</th>\n",
       "      <th>EXT_SOURCE_2</th>\n",
       "      <th>...</th>\n",
       "      <th>DEF_30_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>OBS_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DEF_60_CNT_SOCIAL_CIRCLE</th>\n",
       "      <th>DAYS_LAST_PHONE_CHANGE</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>3.075110e+05</td>\n",
       "      <td>307499.000000</td>\n",
       "      <td>3.072330e+05</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>307511.000000</td>\n",
       "      <td>104582.000000</td>\n",
       "      <td>307509.000000</td>\n",
       "      <td>134133.000000</td>\n",
       "      <td>3.068510e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>306490.000000</td>\n",
       "      <td>307510.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "      <td>265992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.687979e+05</td>\n",
       "      <td>5.990260e+05</td>\n",
       "      <td>27108.573909</td>\n",
       "      <td>5.383962e+05</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>-4986.120328</td>\n",
       "      <td>12.061091</td>\n",
       "      <td>2.152665</td>\n",
       "      <td>0.502130</td>\n",
       "      <td>5.143927e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.143421</td>\n",
       "      <td>1.405292</td>\n",
       "      <td>0.100049</td>\n",
       "      <td>-962.858788</td>\n",
       "      <td>0.006402</td>\n",
       "      <td>0.007000</td>\n",
       "      <td>0.034362</td>\n",
       "      <td>0.267395</td>\n",
       "      <td>0.265474</td>\n",
       "      <td>1.899974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.371231e+05</td>\n",
       "      <td>4.024908e+05</td>\n",
       "      <td>14493.737315</td>\n",
       "      <td>3.694465e+05</td>\n",
       "      <td>0.013831</td>\n",
       "      <td>3522.886321</td>\n",
       "      <td>11.944812</td>\n",
       "      <td>0.910682</td>\n",
       "      <td>0.211062</td>\n",
       "      <td>1.910602e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.446698</td>\n",
       "      <td>2.379803</td>\n",
       "      <td>0.362291</td>\n",
       "      <td>826.808487</td>\n",
       "      <td>0.083849</td>\n",
       "      <td>0.110757</td>\n",
       "      <td>0.204685</td>\n",
       "      <td>0.916002</td>\n",
       "      <td>0.794056</td>\n",
       "      <td>1.869295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.565000e+04</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>1615.500000</td>\n",
       "      <td>4.050000e+04</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>-24672.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014568</td>\n",
       "      <td>8.173617e-08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4292.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.125000e+05</td>\n",
       "      <td>2.700000e+05</td>\n",
       "      <td>16524.000000</td>\n",
       "      <td>2.385000e+05</td>\n",
       "      <td>0.010006</td>\n",
       "      <td>-7479.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.334007</td>\n",
       "      <td>3.924574e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1570.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.471500e+05</td>\n",
       "      <td>5.135310e+05</td>\n",
       "      <td>24903.000000</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>0.018850</td>\n",
       "      <td>-4504.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.505998</td>\n",
       "      <td>5.659614e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-757.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.025000e+05</td>\n",
       "      <td>8.086500e+05</td>\n",
       "      <td>34596.000000</td>\n",
       "      <td>6.795000e+05</td>\n",
       "      <td>0.028663</td>\n",
       "      <td>-2010.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.675053</td>\n",
       "      <td>6.636171e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-274.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.170000e+08</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>258025.500000</td>\n",
       "      <td>4.050000e+06</td>\n",
       "      <td>0.072508</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.962693</td>\n",
       "      <td>8.549997e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AMT_INCOME_TOTAL    AMT_CREDIT    AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "count      3.075110e+05  3.075110e+05  307499.000000     3.072330e+05   \n",
       "mean       1.687979e+05  5.990260e+05   27108.573909     5.383962e+05   \n",
       "std        2.371231e+05  4.024908e+05   14493.737315     3.694465e+05   \n",
       "min        2.565000e+04  4.500000e+04    1615.500000     4.050000e+04   \n",
       "25%        1.125000e+05  2.700000e+05   16524.000000     2.385000e+05   \n",
       "50%        1.471500e+05  5.135310e+05   24903.000000     4.500000e+05   \n",
       "75%        2.025000e+05  8.086500e+05   34596.000000     6.795000e+05   \n",
       "max        1.170000e+08  4.050000e+06  258025.500000     4.050000e+06   \n",
       "\n",
       "       REGION_POPULATION_RELATIVE  DAYS_REGISTRATION    OWN_CAR_AGE  \\\n",
       "count               307511.000000      307511.000000  104582.000000   \n",
       "mean                     0.020868       -4986.120328      12.061091   \n",
       "std                      0.013831        3522.886321      11.944812   \n",
       "min                      0.000290      -24672.000000       0.000000   \n",
       "25%                      0.010006       -7479.500000       5.000000   \n",
       "50%                      0.018850       -4504.000000       9.000000   \n",
       "75%                      0.028663       -2010.000000      15.000000   \n",
       "max                      0.072508           0.000000      91.000000   \n",
       "\n",
       "       CNT_FAM_MEMBERS   EXT_SOURCE_1  EXT_SOURCE_2  \\\n",
       "count    307509.000000  134133.000000  3.068510e+05   \n",
       "mean          2.152665       0.502130  5.143927e-01   \n",
       "std           0.910682       0.211062  1.910602e-01   \n",
       "min           1.000000       0.014568  8.173617e-08   \n",
       "25%           2.000000       0.334007  3.924574e-01   \n",
       "50%           2.000000       0.505998  5.659614e-01   \n",
       "75%           3.000000       0.675053  6.636171e-01   \n",
       "max          20.000000       0.962693  8.549997e-01   \n",
       "\n",
       "                  ...              DEF_30_CNT_SOCIAL_CIRCLE  \\\n",
       "count             ...                         306490.000000   \n",
       "mean              ...                              0.143421   \n",
       "std               ...                              0.446698   \n",
       "min               ...                              0.000000   \n",
       "25%               ...                              0.000000   \n",
       "50%               ...                              0.000000   \n",
       "75%               ...                              0.000000   \n",
       "max               ...                             34.000000   \n",
       "\n",
       "       OBS_60_CNT_SOCIAL_CIRCLE  DEF_60_CNT_SOCIAL_CIRCLE  \\\n",
       "count             306490.000000             306490.000000   \n",
       "mean                   1.405292                  0.100049   \n",
       "std                    2.379803                  0.362291   \n",
       "min                    0.000000                  0.000000   \n",
       "25%                    0.000000                  0.000000   \n",
       "50%                    0.000000                  0.000000   \n",
       "75%                    2.000000                  0.000000   \n",
       "max                  344.000000                 24.000000   \n",
       "\n",
       "       DAYS_LAST_PHONE_CHANGE  AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "count           307510.000000               265992.000000   \n",
       "mean              -962.858788                    0.006402   \n",
       "std                826.808487                    0.083849   \n",
       "min              -4292.000000                    0.000000   \n",
       "25%              -1570.000000                    0.000000   \n",
       "50%               -757.000000                    0.000000   \n",
       "75%               -274.000000                    0.000000   \n",
       "max                  0.000000                    4.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "count              265992.000000               265992.000000   \n",
       "mean                    0.007000                    0.034362   \n",
       "std                     0.110757                    0.204685   \n",
       "min                     0.000000                    0.000000   \n",
       "25%                     0.000000                    0.000000   \n",
       "50%                     0.000000                    0.000000   \n",
       "75%                     0.000000                    0.000000   \n",
       "max                     9.000000                    8.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "count              265992.000000              265992.000000   \n",
       "mean                    0.267395                   0.265474   \n",
       "std                     0.916002                   0.794056   \n",
       "min                     0.000000                   0.000000   \n",
       "25%                     0.000000                   0.000000   \n",
       "50%                     0.000000                   0.000000   \n",
       "75%                     0.000000                   0.000000   \n",
       "max                    27.000000                 261.000000   \n",
       "\n",
       "       AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "count               265992.000000  \n",
       "mean                     1.899974  \n",
       "std                      1.869295  \n",
       "min                      0.000000  \n",
       "25%                      0.000000  \n",
       "50%                      1.000000  \n",
       "75%                      3.000000  \n",
       "max                     25.000000  \n",
       "\n",
       "[8 rows x 65 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_num].describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NAME_CONTRACT_TYPE                 0\n",
       "CODE_GENDER                        0\n",
       "FLAG_OWN_CAR                       0\n",
       "FLAG_OWN_REALTY                    0\n",
       "NAME_TYPE_SUITE                 1292\n",
       "NAME_INCOME_TYPE                   0\n",
       "NAME_EDUCATION_TYPE                0\n",
       "NAME_FAMILY_STATUS                 0\n",
       "NAME_HOUSING_TYPE                  0\n",
       "OCCUPATION_TYPE                96391\n",
       "WEEKDAY_APPR_PROCESS_START         0\n",
       "ORGANIZATION_TYPE                  0\n",
       "FONDKAPREMONT_MODE            210295\n",
       "HOUSETYPE_MODE                154297\n",
       "WALLSMATERIAL_MODE            156341\n",
       "EMERGENCYSTATE_MODE           145755\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_categorical].isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>NAME_TYPE_SUITE</th>\n",
       "      <th>NAME_INCOME_TYPE</th>\n",
       "      <th>NAME_EDUCATION_TYPE</th>\n",
       "      <th>NAME_FAMILY_STATUS</th>\n",
       "      <th>NAME_HOUSING_TYPE</th>\n",
       "      <th>OCCUPATION_TYPE</th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
       "      <th>ORGANIZATION_TYPE</th>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <th>HOUSETYPE_MODE</th>\n",
       "      <th>WALLSMATERIAL_MODE</th>\n",
       "      <th>EMERGENCYSTATE_MODE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>306219</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>211120</td>\n",
       "      <td>307511</td>\n",
       "      <td>307511</td>\n",
       "      <td>97216</td>\n",
       "      <td>153214</td>\n",
       "      <td>151170</td>\n",
       "      <td>161756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Unaccompanied</td>\n",
       "      <td>Working</td>\n",
       "      <td>Secondary / secondary special</td>\n",
       "      <td>Married</td>\n",
       "      <td>House / apartment</td>\n",
       "      <td>Laborers</td>\n",
       "      <td>TUESDAY</td>\n",
       "      <td>Business Entity Type 3</td>\n",
       "      <td>reg oper account</td>\n",
       "      <td>block of flats</td>\n",
       "      <td>Panel</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>278232</td>\n",
       "      <td>202448</td>\n",
       "      <td>202924</td>\n",
       "      <td>213312</td>\n",
       "      <td>248526</td>\n",
       "      <td>158774</td>\n",
       "      <td>218391</td>\n",
       "      <td>196432</td>\n",
       "      <td>272868</td>\n",
       "      <td>55186</td>\n",
       "      <td>53901</td>\n",
       "      <td>67992</td>\n",
       "      <td>73830</td>\n",
       "      <td>150503</td>\n",
       "      <td>66040</td>\n",
       "      <td>159428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "count              307511      307511       307511          307511   \n",
       "unique                  2           3            2               2   \n",
       "top            Cash loans           F            N               Y   \n",
       "freq               278232      202448       202924          213312   \n",
       "\n",
       "       NAME_TYPE_SUITE NAME_INCOME_TYPE            NAME_EDUCATION_TYPE  \\\n",
       "count           306219           307511                         307511   \n",
       "unique               7                8                              5   \n",
       "top      Unaccompanied          Working  Secondary / secondary special   \n",
       "freq            248526           158774                         218391   \n",
       "\n",
       "       NAME_FAMILY_STATUS  NAME_HOUSING_TYPE OCCUPATION_TYPE  \\\n",
       "count              307511             307511          211120   \n",
       "unique                  6                  6              18   \n",
       "top               Married  House / apartment        Laborers   \n",
       "freq               196432             272868           55186   \n",
       "\n",
       "       WEEKDAY_APPR_PROCESS_START       ORGANIZATION_TYPE FONDKAPREMONT_MODE  \\\n",
       "count                      307511                  307511              97216   \n",
       "unique                          7                      58                  4   \n",
       "top                       TUESDAY  Business Entity Type 3   reg oper account   \n",
       "freq                        53901                   67992              73830   \n",
       "\n",
       "        HOUSETYPE_MODE WALLSMATERIAL_MODE EMERGENCYSTATE_MODE  \n",
       "count           153214             151170              161756  \n",
       "unique               3                  7                   2  \n",
       "top     block of flats              Panel                  No  \n",
       "freq            150503              66040              159428  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[col_categorical].describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FLAG_DOCUMENT_17',\n",
       " 'FLAG_DOCUMENT_12',\n",
       " 'FLAG_WORK_PHONE',\n",
       " 'HOUSETYPE_MODE',\n",
       " 'NAME_EDUCATION_TYPE',\n",
       " 'FLAG_MOBIL',\n",
       " 'FLAG_DOCUMENT_18',\n",
       " 'FLAG_OWN_REALTY',\n",
       " 'FLAG_DOCUMENT_4',\n",
       " 'FLAG_PHONE',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'FLAG_DOCUMENT_6',\n",
       " 'FLAG_DOCUMENT_9',\n",
       " 'NAME_CONTRACT_TYPE',\n",
       " 'FLAG_DOCUMENT_15',\n",
       " 'NAME_TYPE_SUITE',\n",
       " 'OCCUPATION_TYPE',\n",
       " 'FLAG_DOCUMENT_11',\n",
       " 'NAME_HOUSING_TYPE',\n",
       " 'FLAG_DOCUMENT_5',\n",
       " 'FLAG_DOCUMENT_19',\n",
       " 'WEEKDAY_APPR_PROCESS_START',\n",
       " 'FLAG_DOCUMENT_7',\n",
       " 'FLAG_DOCUMENT_21',\n",
       " 'FLAG_DOCUMENT_14',\n",
       " 'FLAG_OWN_CAR',\n",
       " 'FLAG_DOCUMENT_8',\n",
       " 'FLAG_DOCUMENT_20',\n",
       " 'CODE_GENDER',\n",
       " 'FLAG_DOCUMENT_16',\n",
       " 'ORGANIZATION_TYPE',\n",
       " 'FLAG_EMAIL',\n",
       " 'FONDKAPREMONT_MODE',\n",
       " 'NAME_INCOME_TYPE',\n",
       " 'EMERGENCYSTATE_MODE',\n",
       " 'FLAG_DOCUMENT_2',\n",
       " 'FLAG_DOCUMENT_3',\n",
       " 'FLAG_DOCUMENT_10',\n",
       " 'FLAG_DOCUMENT_13',\n",
       " 'WALLSMATERIAL_MODE',\n",
       " 'FLAG_CONT_MOBILE',\n",
       " 'NAME_FAMILY_STATUS']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_categorical_feats = list(set(col_categorical + [col for col in df_train.columns if col[:4] == \"FLAG\"]))\n",
    "all_categorical_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, precision_recall_curve, auc, average_precision_score, accuracy_score\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV, cross_val_score\n",
    "\n",
    "for col in all_categorical_feats:\n",
    "    df_train[col] = df_train[col].astype('category') \n",
    "X = df_train.drop('TARGET', axis=1) \n",
    "Y = df_train['TARGET']\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=123, stratify=Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# todo: normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.270575578267886, roc_auc=0.7504852060821952, total=  19.1s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   26.8s remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27792797664107277, roc_auc=0.7571476735557036, total=  18.4s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   52.8s remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27387715725158107, roc_auc=0.7576641119719532, total=  18.7s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  1.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2750482729028106, roc_auc=0.7538804876079093, total=  18.5s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  1.8min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27197098976109213, roc_auc=0.753030883655575, total=  19.4s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  2.2min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.26962659626057434, roc_auc=0.7498657026334463, total=  22.9s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:  2.8min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27711300016243434, roc_auc=0.7566930541203859, total=  21.2s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:  3.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2773722627737226, roc_auc=0.7588141865933522, total=  20.8s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  3.8min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27352545111769455, roc_auc=0.7540984118482922, total=  20.1s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  4.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.1, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2722939317658401, roc_auc=0.7530609700034492, total=  19.9s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  4.7min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.26902939991945224, roc_auc=0.7400589445597404, total=  20.3s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:  5.2min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27958097200755627, roc_auc=0.7491410468014104, total=  18.8s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:  5.6min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27483856349835734, roc_auc=0.7499878933654307, total=  17.4s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:  6.0min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2749310661764706, roc_auc=0.7442712142484846, total=  16.2s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:  6.4min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2754689014309332, roc_auc=0.7489034987377762, total=  18.2s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  6.9min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27002679742288616, roc_auc=0.7423874873395218, total=  17.9s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:  7.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27938922631549074, roc_auc=0.7512057094777304, total=  17.7s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:  7.7min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2776332164271976, roc_auc=0.7517601265457806, total=  16.9s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:  8.1min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27619645199931553, roc_auc=0.7468251193540171, total=  18.7s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:  8.6min remaining:    0.0s\n",
      "[CV] learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.2, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2744183400648501, roc_auc=0.7438350651825597, total=  20.5s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:  9.1min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2700173933905116, roc_auc=0.7327157892111906, total=  18.1s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:  9.5min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.27706510010790075, roc_auc=0.7382133924871848, total=  17.4s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:  9.9min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2735580346546404, roc_auc=0.7379482989371322, total=  19.1s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed: 10.4min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.26991733556966574, roc_auc=0.7364086802136167, total=  18.3s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed: 10.9min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=0, max_depth=5, n_estimators=200, f1=0.2682518819452742, roc_auc=0.7333964272715171, total=  19.1s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed: 11.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.2683380315480118, roc_auc=0.7329023260101275, total=  19.6s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed: 11.8min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.277624475104979, roc_auc=0.7400486580950579, total=  21.6s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed: 12.3min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27099396330165565, roc_auc=0.7369058369898153, total=  18.8s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed: 12.7min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.27099168112992994, roc_auc=0.7343569805459281, total=  21.4s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed: 13.2min remaining:    0.0s\n",
      "[CV] learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n",
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  learning_rate=0.3, max_delta_step=2, max_depth=5, n_estimators=200, f1=0.273806696746616, roc_auc=0.7348560187068172, total=  21.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 13.7min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed: 13.7min finished\n"
     ]
    }
   ],
   "source": [
    "estimator = lgb.LGBMClassifier(boosting_type='gbdt', class_weight='balanced', silent=False)\n",
    "param_grid = {\n",
    "    'n_estimators': [200],\n",
    "    'max_depth':[5],\n",
    "    'max_delta_step':[0,2],\n",
    "    'learning_rate': [0.1,0.2,0.3] \n",
    "}\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=7)\n",
    "gbm = GridSearchCV(estimator, param_grid,  cv=kfold,  verbose=100, scoring=['f1','roc_auc'], refit='roc_auc')\n",
    "grid_result = gbm.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.754506 using {'learning_rate': 0.1, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.754442 (0.002671) with: {'learning_rate': 0.1, 'max_delta_step': 0, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.754506 (0.003070) with: {'learning_rate': 0.1, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.746473 (0.003778) with: {'learning_rate': 0.2, 'max_delta_step': 0, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.747203 (0.003781) with: {'learning_rate': 0.2, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.735737 (0.002284) with: {'learning_rate': 0.3, 'max_delta_step': 0, 'max_depth': 5, 'n_estimators': 200}\n",
      "0.735814 (0.002476) with: {'learning_rate': 0.3, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1044x1944 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_roc_auc']\n",
    "stds = grid_result.cv_results_['std_test_roc_auc']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "        print(\"%f (%f) with: %r\" % (mean, stdev, param))\n",
    "\n",
    "lgb.plot_importance(grid_result.best_estimator_)\n",
    "plt.gcf().set_size_inches(14.5, 27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "  if diff:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "Y_pred = grid_result.predict(X_test)\n",
    "\n",
    "print('Confusion Matrix:')\n",
    "\n",
    "df_cm = pd.DataFrame(confusion_matrix(Y_test, Y_pred))\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.heatmap(df_cm, annot=True, fmt=\"d\", ax=ax)\n",
    "ax.set_ylabel('True label');\n",
    "ax.set_xlabel('Predicted label');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_xgb = pd.DataFrame(list(grid_result.predict_proba(X_test)))\n",
    "fpr_xgb, tpr_xgb, thresholds_xgb = roc_curve(Y_test, prob_xgb.iloc[:,1])\n",
    "roc_auc_xgb = auc(fpr_xgb, tpr_xgb)\n",
    "p_xgb,r_xgb,thre_xgb = precision_recall_curve(Y_test,prob_xgb.iloc[:,1])\n",
    "average_p_xgb = average_precision_score(Y_test, prob_xgb.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,6))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "ax1.set_xlim([-0.05,1.05])\n",
    "ax1.set_ylim([-0.05,1.05])\n",
    "ax1.set_xlabel('False Positive Rate')\n",
    "ax1.set_ylabel('True Positive Rate')\n",
    "ax1.set_title('ROC Curve')\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.set_xlim([-0.05,1.05])\n",
    "ax2.set_ylim([-0.05,1.05])\n",
    "ax2.set_xlabel('Recall')\n",
    "ax2.set_ylabel('Precision')\n",
    "ax2.set_title('PR Curve')\n",
    "\n",
    "ax1.plot(fpr_xgb, tpr_xgb, lw=1, label='XGBoost: area = %0.2f'%roc_auc_xgb)\n",
    "\n",
    "ax2.plot(r_xgb, p_xgb, lw=1, label='XGBoost: area = %0.2f'%average_p_xgb)\n",
    "\n",
    "ax1.legend(loc='lower right')    \n",
    "ax2.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate accuracy for xgboost\n",
    "score_xgb = grid_result.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Binary Accuracy</th>\n",
       "      <th>AUC of ROC</th>\n",
       "      <th>AUC of PR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.754267</td>\n",
       "      <td>0.754267</td>\n",
       "      <td>0.237291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Model  Binary Accuracy  AUC of ROC  AUC of PR\n",
       "0  XGBoost         0.754267    0.754267   0.237291"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_lr = 0\n",
    "roc_auc_lr = 0\n",
    "average_p_lr = 0\n",
    "\n",
    "result = {'Model':['XGBoost'],\n",
    "          'Binary Accuracy':[score_xgb],\n",
    "          'AUC of ROC':[roc_auc_xgb],\n",
    "          'AUC of PR':[average_p_xgb]}\n",
    "pd.DataFrame(result,columns=['Model', 'Binary Accuracy', 'AUC of ROC', 'AUC of PR'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kaggle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading application_test.csv.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>AMT_GOODS_PRICE</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100001</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>568800.0</td>\n",
       "      <td>20560.5</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100005</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>222768.0</td>\n",
       "      <td>17370.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100013</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>663264.0</td>\n",
       "      <td>69777.0</td>\n",
       "      <td>630000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR FLAG_OWN_REALTY  \\\n",
       "0      100001         Cash loans           F            N               Y   \n",
       "1      100005         Cash loans           M            N               Y   \n",
       "2      100013         Cash loans           M            Y               Y   \n",
       "\n",
       "   CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  AMT_GOODS_PRICE  \\\n",
       "0             0          135000.0    568800.0      20560.5         450000.0   \n",
       "1             0           99000.0    222768.0      17370.0         180000.0   \n",
       "2             0          202500.0    663264.0      69777.0         630000.0   \n",
       "\n",
       "              ...             FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                            0                0   \n",
       "1             ...                            0                0   \n",
       "2             ...                            0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                        0.0                         0.0   \n",
       "1                        0.0                         0.0   \n",
       "2                        0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        1.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         0.0  \n",
       "1                         3.0  \n",
       "2                         4.0  \n",
       "\n",
       "[3 rows x 121 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = csv_loader(\"application_test.csv\")\n",
    "df_test.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in all_categorical_feats:\n",
    "    df_test[col] = df_test[col].astype('category') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Refit\n",
    "best_param = {'learning_rate': 0.1, 'max_delta_step': 2, 'max_depth': 5, 'n_estimators': 200}\n",
    "est = lgb.LGBMClassifier(boosting_type='gbdt', class_weight='balanced', silent=False, **best_param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = est.fit(X, Y)\n",
    "prediction = pd.DataFrame(list(final_model.predict_proba(df_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test[\"TARGET\"] = prediction.iloc[:,1]\n",
    "df_test[[\"SK_ID_CURR\",\"TARGET\"]].to_csv(\"./results/lgb_model_1st.csv\", index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
